On the value of parameter tuning in stacking ensemble model for software regression test effort estimation

A type of software testing, regression testing is often costly and labour-intensive. As such, multiple corporations have intensified efforts to estimate the amount of effort required. However, frequent alterations in software projects impact the precision of software regression test effort estimation (SRTEE), which increases the difficulty of managing software projects. Therefore, machine learning (ML) has increasingly been used to develop more accurate SRTEEs. The estimation process of a software project comprises inputs, the model, and outputs. This present study examines the quality of estimation inputs and the model required to deliver accurate estimation outputs. An SRTEE that uses the stacking ensemble model (StackSRTEE) was developed to increase the precision of SRTEE. It consisted of the three most common ML methods, namely neural networks, support vector regression, and decision tree regression. The grid search (GS) technique was then used to tune the hyperparameters of the StackSRTEE before it was trained and tested using a dataset from the International Software Benchmarking Standards Group (ISBSG) repository. The size of the functional change; specifically, enhancement; was used as the primary independent variable to improve the inputs of the StackSRTEE model. With the appropriate features; such as the functional change size of an enhancement; (1) the proposed StackSRTEE model yielded higher accuracy than the three individual ML methods on their own, (2) using GS to tune and set the individual ML methods increased the precision of the SRTEE outputs, and (3) the StackSRTEE-based GS tuning yielded estimations that were more precise.